基于改进注意力机制和曲面微分几何的2阶段点云重建OA北大核心CSTPCD

The straight-through structure of direct prediction of point clouds in a single stage will lead to relatively sparse point clouds,unclear details and uneven distribution of results.Therefore,a 2-stage dense point cloud reconstruction network based on improved attention mechanism and local surface differential geometry is proposed,which can realize phased prediction of dense point clouds with different resolutions and uniform distribution.Firstly,the coordinate attention mechanism is improved by embedding to improve the network's ability to perceive the direction of the target and capture the coordinate information in the input image.Then,the sparse point cloud of the target is predicted,and the dense connection features of the sparse point cloud are extracted to obtain the structural perception information describing the sparse point cloud.Finally,the local surface differential geometry is used to complete the point cloud expansion,and then generate a dense point cloud with uniform distribution.The experimental results show that compared with 3D-FEGNet,the proposed dense point cloud reconstruction network reduces by 25.4%and 25.1%on CD and EMD,respectively.The three-dimensional dimensional errors of point clouds obtained by experiments with real objects are all less than 1.5 mm.